CN112008495B - Cutter damage identification method based on vibration monitoring - Google Patents

Cutter damage identification method based on vibration monitoring Download PDF

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CN112008495B
CN112008495B CN202010735535.9A CN202010735535A CN112008495B CN 112008495 B CN112008495 B CN 112008495B CN 202010735535 A CN202010735535 A CN 202010735535A CN 112008495 B CN112008495 B CN 112008495B
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mean square
root mean
vibration
cutter
value
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CN112008495A (en
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姜振喜
宋戈
朱绍维
孙超
李卫东
王灿
王伟
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Chengdu Aircraft Industrial Group Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
    • B23Q17/0957—Detection of tool breakage

Abstract

The application relates to the field of numerical control machining of aircraft structural parts, in particular to a hole position control method for drilling of an aircraft structural part, which is used for monitoring damage of a cutter in the machining process of the aircraft structural part made of a difficult-to-machine material and is used when a cutter is replaced by a structural part machining program every timeStarting the vibration signal acquisition and then setting the time interval deltatAnd calculating the root mean square value of each rotation of the vibration signal calibration section, and takingmSegment signal calibration mean root mean squareM 1Continuing to judge the transition section of the vibration signal and judging the mean value of the root mean square of each section of the signalM 1Whether the deviation of (2) is greater than the allowable fluctuation rangeKIn the range ofKContinuously executing the transition section judgment within the range of more than the rangeKEntering a tool damage state confirmation stagemSegment signal calculation root mean square mean valueM 2Judgment ofM 2AndM 1whether or not the deviation exceedsM 1Is/are as followsBAnd monitoring is continuously carried out if the number of the tools exceeds the preset value, and if the number of the tools exceeds the preset value, a new tool is replaced for processing, so that the online identification of the tool damage in the processing process of the aircraft structural part is realized, and the problem of part processing quality caused by the tool damage is avoided.

Description

Cutter damage identification method based on vibration monitoring
Technical Field
The application relates to the field of numerical control machining of aircraft structural parts, in particular to a cutter damage identification method based on vibration monitoring.
Background
In metal cutting, the tool is gradually worn, even broken, and broken as the use time increases. Due to direct contact with the workpiece, excessive wear and breakage of the tool will reduce the dimensional accuracy and surface quality of the part, and even cause the part to be scrapped (for example, the part is burned after the blade is broken). Therefore, in the machining process, attention needs to be paid to the state of the tool from time to time, and the tool needs to be replaced in time when the tool is worn to a certain extent or damaged.
At present, in the numerical control machining process of an aircraft structural part, the state of a cutter is mainly judged by an operator through experience, the influence of human factors is large, and the timely response to some abnormal conditions is difficult. As a result, part quality problems due to excessive wear/breakage of the tool often occur. Commercial cutter monitoring systems such as ARTIS and the like are already on the market and are well applied in the automobile industry, but are easily affected by fluctuation of a machining state in the machining process of complex parts such as aircraft structural parts and the like, and false alarms are frequently generated to affect normal production. With modern manufacturing automation and the greatly increased complexity of tools and machining parts, online monitoring of tool states is an urgent problem to be solved.
Disclosure of Invention
Aiming at the problem that the damage of the cutter is difficult to identify in the monitoring of the machining process of the complex structural part of the airplane, a cutter damage identification method based on vibration monitoring is provided.
In order to achieve the technical effects, the technical scheme of the application is as follows:
a cutter damage identification method based on vibration monitoring comprises the following steps:
the first step is as follows: replacing a new tool in a structural member machining program to start vibration signal acquisition;
in order to monitor damage and failure of a numerical control machining tool for an airplane structural part made of a difficult-to-machine material in the using process and acquire vibration signals in the numerical control machining process, an acceleration sensor is used, and the position of the sensor can be arranged on a machine tool spindle or a tool.
In order to monitor the use condition of each cutter, the acquisition of vibration signals needs to be matched with the use process of the cutter, so that the acquisition of the vibration signals is started when a new cutter is used, and the acquisition of the vibration signals is closed after the cutter is used. And according to a tool calling instruction in the NC program for machining the structural part, restarting the vibration signal acquisition every time when a new tool is replaced.
The second step is that: calculating the mean value of the root mean square of each rotation of the vibration signal calibration section;
and in the using process of the cutter, evaluating the state of the cutter according to the root mean square value of each rotation of the acquired vibration signal. When the new cutter is just used, the cutting edge of the cutter gradually enters a stable abrasion state, the cutting edge state is good at the moment, the quality of a machined part meets the design requirement, and the cutting edge state is used as a vibration signal calibration section at the stage.
Setting a signal processing time interval delta t, namely calculating the vibration signal of the time interval and outputting a root mean square value RMS (j) every time the delta t time interval passes.
△t＝u×t0＝u×60/n
Wherein u is the number of revolutions and n is the magnitude of the revolution.
The method for calculating the root mean square of the vibration amplitude of the vibration signal in each cutting time is as follows:
the number of samples per revolution, N, is calculated as:
Figure GDA0003128786170000021
wherein f issIs the sampling frequency of the vibration signal.
The root mean square rms (j) is calculated as:
Figure GDA0003128786170000022
wherein x is the vibration amplitude in a certain direction, and j is the counted number of the cutting revolution of the cutter.
Selecting m sections of delta t vibration signals, and calibrating the average value of root-mean-square of each rotation:
Figure GDA0003128786170000023
the third step: judging a transition section of the vibration signal;
in the machining process of the structural part, the tool feed track is constantly changed under the influence of the structural characteristics of the part, and the tool feed track is represented as the change of a straight line section and a curve section. In the cutting process, the contact angle between the cutter and the workpiece is changed continuously, so that the cutting amount is changed along with time, and the amplitude of vibration is influenced by the change of the processing amount to a certain extent.
And setting an allowable fluctuation range K of the root mean square value, aiming at enhancing the running speed of the recognition algorithm so as to improve the instantaneity of monitoring, wherein when the part machining process is monitored on line, if the data transmission and data processing time is long, the time interval of vibration data processing is long, the deviation between the time when the data processing result is calculated and the actual machining time is large, and the machining vibration cannot be monitored in time.
And (3) the allowable fluctuation range K prevents the vibration amplitude change caused by the cutter track change in the cutter damage online identification process from triggering a cutter damage state confirmation stage, and the data processing only needs to execute 1-time delta t time interval vibration signal calculation during vibration signal transition section judgment, so that the algorithm operation time is reduced.
And continuously intercepting the vibration signals at the time intervals of delta t, and respectively calculating the average value A of root mean square RMS (j) of each rotation.
Determine | A-M for each Δ t time interval1If the value of | is larger than K, the transition section judgment is continuously executed within the range of K, and the cutter damage state confirmation stage is started when the value of | is larger than the range of K.
The fourth step: confirming the damage state of the cutter;
selecting M sections of delta t time intervals of vibration signals, and calculating the average value M of root mean square of each rotation2。
Judgment | M2-M1Whether the value of | is greater than B × M1And B is a set root mean square deviation coefficient, if the root mean square deviation coefficient does not exceed the set root mean square deviation coefficient, the monitoring is continued, and if the root mean square deviation coefficient exceeds the set root mean square deviation coefficient, a new cutter is replaced for machining.
The invention has the beneficial effects that:
the method starts vibration signal acquisition when a new cutter is replaced by a structural part machining program every time, then sets a time interval delta t, calculates the root mean square value of each rotation of a vibration signal calibration section, and takes M sections of signals to calibrate the root mean square average value M1Continuously executing the vibration signal transition section discrimination, and judging the root mean square average value and M of each section of signal1Whether the deviation is larger than an allowable fluctuation range K or not, continuously executing transition section judgment within the range K, entering a tool damage state confirmation stage when the deviation is larger than the allowable fluctuation range K, and taking M sections of signals to calculate a root mean square average value M2Judgment of M2And M1Is deviated byWhether or not it exceeds M1And B times of the reference value is not exceeded, the reference value is continuously monitored, if the reference value exceeds the reference value, a new cutter is replaced for machining, the online identification of the damage of the cutter in the machining process of the aircraft structural part is realized, and the problem of part machining quality caused by the damage of the cutter is avoided.
Drawings
Fig. 1 is a flow chart of a tool breakage online identification method.
Fig. 2 is a schematic diagram of a part machining feed trajectory.
Fig. 3 is a graph of root mean square of the vibration signal as a function of machining time.
In the drawings, 1, part; 2. a feed trajectory; 3. and (4) a cutter.
Detailed Description
The invention will be further described with reference to the following figures and examples, but the invention is not limited to these examples.
Example 1
A cutter damage identification method based on vibration monitoring comprises the following steps:
the first step is as follows: replacing a new tool in a structural member machining program to start vibration signal acquisition;
in order to monitor damage and failure of a numerical control machining tool for an airplane structural part made of a difficult-to-machine material in the using process and acquire vibration signals in the numerical control machining process, an acceleration sensor is used, and the position of the sensor can be arranged on a machine tool spindle or a tool.
In order to monitor the use condition of each cutter, the acquisition of vibration signals needs to be matched with the use process of the cutter, so that the acquisition of the vibration signals is started when a new cutter is used, and the acquisition of the vibration signals is closed after the cutter is used. And according to a tool calling instruction in the NC program for machining the structural part, restarting the vibration signal acquisition every time when a new tool is replaced.
The second step is that: calculating the mean value of the root mean square of each rotation of the vibration signal calibration section;
and in the using process of the cutter, evaluating the state of the cutter according to the root mean square value of each rotation of the acquired vibration signal. When the new cutter is just used, the cutting edge of the cutter gradually enters a stable abrasion state, the cutting edge state is good at the moment, the quality of a machined part meets the design requirement, and the cutting edge state is used as a vibration signal calibration section at the stage.
Setting a signal processing time interval delta t, namely calculating the vibration signal of the time interval and outputting a root mean square value RMS (j) every time the delta t time interval passes.
△t＝u×t0＝u×60/n
Wherein u is the number of revolutions and n is the magnitude of the revolution.
The method for calculating the root mean square of the vibration amplitude of the vibration signal in each cutting time is as follows:
the number of samples per revolution, N, is calculated as:
Figure GDA0003128786170000041
wherein f issIs the sampling frequency of the vibration signal.
The root mean square rms (j) is calculated as:
Figure GDA0003128786170000042
wherein x is the vibration amplitude in a certain direction, and j is the counted number of the cutting revolution of the cutter.
Selecting m sections of delta t vibration signals, and calibrating the average value of root-mean-square of each rotation:
Figure GDA0003128786170000051
the third step: judging a transition section of the vibration signal;
in the machining process of the structural part, the tool feed track is constantly changed under the influence of the structural characteristics of the part, and the tool feed track is represented as the change of a straight line section and a curve section. In the cutting process, the contact angle between the cutter and the workpiece is changed continuously, so that the cutting amount is changed along with time, and the amplitude of vibration is influenced by the change of the processing amount to a certain extent.
And setting an allowable fluctuation range K of the root mean square value, aiming at enhancing the running speed of the recognition algorithm so as to improve the instantaneity of monitoring, wherein when the part machining process is monitored on line, if the data transmission and data processing time is long, the time interval of vibration data processing is long, the deviation between the time when the data processing result is calculated and the actual machining time is large, and the machining vibration cannot be monitored in time.
And (3) the allowable fluctuation range K prevents the vibration amplitude change caused by the cutter track change in the cutter damage online identification process from triggering a cutter damage state confirmation stage, and the data processing only needs to execute 1-time delta t time interval vibration signal calculation during vibration signal transition section judgment, so that the algorithm operation time is reduced.
And continuously intercepting the vibration signals at the time intervals of delta t, and respectively calculating the average value A of root mean square RMS (j) of each rotation.
Determine | A-M for each Δ t time interval1If the value of | is larger than K, the transition section judgment is continuously executed within the range of K, and the cutter damage state confirmation stage is started when the value of | is larger than the range of K.
The fourth step: confirming the damage state of the cutter;
selecting M sections of delta t time intervals of vibration signals, and calculating the average value M of root mean square of each rotation2。
Judgment | M2-M1Whether the value of | is greater than B × M1And B is a set root mean square deviation coefficient, if the root mean square deviation coefficient does not exceed the set root mean square deviation coefficient, the monitoring is continued, and if the root mean square deviation coefficient exceeds the set root mean square deviation coefficient, a new cutter is replaced for machining.
Example 2
The invention provides a cutter damage identification method based on vibration monitoring, which comprises the following specific implementation contents:
s1: and replacing the machining program of the structural part with a new cutter to start vibration signal acquisition.
The vibration signals in the numerical control machining process are collected by using an acceleration sensor, and the position of the sensor can be arranged on a machine tool spindle or a tool. In order to monitor the use condition of each cutter, the acquisition of vibration signals needs to be matched with the use process of the cutter, so that the acquisition of the vibration signals is started when a new cutter is used, and the acquisition of the vibration signals is closed after the cutter is used. And according to a tool calling instruction in the NC program for machining the structural part, restarting the vibration signal acquisition every time when a new tool is replaced.
S2: and calculating the mean value of the root mean square of each rotation of the vibration signal calibration section.
When the new cutter is just used, the cutting edge of the cutter gradually enters a stable abrasion state, the cutting edge state is good at the moment, the quality of a machined part meets the design requirement, and the cutting edge state is used as a vibration signal calibration section at the stage.
Setting a signal processing time interval delta t, namely calculating the vibration signal of the time interval and outputting a root mean square value RMS (j) every time the delta t time interval passes.
△t＝u×t0＝u×60/n
Wherein u is the number of revolutions and n is the magnitude of the revolution.
The rotation number u is determined and adjusted according to the rotation speed n, the machined surface is controlled to be small within the time range of delta t, and even if surface damage caused by tool tipping occurs, the surface damage can be corrected through a grinding process. In the present example, when the rotation speed n is 1200r/min, the feed speed F is 800mm/min, and the rotation speed u is set to 10, the distance L is fed at the time Δ t1＝F×Δt＝6.66mm。
The method for calculating the root mean square of the vibration amplitude of the vibration signal in each cutting time is as follows:
the number of samples per revolution, N, is calculated as:
Figure GDA0003128786170000061
wherein f issIs the sampling frequency of the vibration signal.
The root mean square rms (j) is calculated as:
Figure GDA0003128786170000062
wherein x is the vibration amplitude in a certain direction, and j is the counted number of the cutting revolution of the cutter.
Selecting m segments of Δ t vibration signal, m being 10 in this example, then m segments of Δ t time range feeding distance L2The mean root mean square per revolution is calibrated at 66.6 mm:
Figure GDA0003128786170000063
s3: and executing vibration signal transition section judgment.
In the machining process of the structural part, the tool feed track is constantly changed under the influence of the structural characteristics of the part, and the tool feed track is represented as the change of a straight line section and a curve section. In the cutting process, the contact angle between the cutter and the workpiece is changed continuously, so that the cutting amount is changed along with time, and the amplitude of vibration is influenced by the change of the processing amount to a certain extent.
Thus, by setting an allowable fluctuation range K of the root mean square value, the size of the fluctuation range K being related to the settings of the cutting parameters and the feed path, and keeping the cutting parameters and the feed path settings consistent to a certain degree by means of the customized programming specification, the allowable fluctuation range K can be determined, in this example, K is 0.4.
The allowable fluctuation range K is set to enhance the running speed of the recognition algorithm so as to improve the monitoring instantaneity, when the part machining process is monitored on line, if the data transmission and data processing time is long, the time interval of vibration data processing is long, the deviation between the time of completing the data processing result calculation and the actual machining time is large, and the machining vibration cannot be monitored immediately.
And (3) the allowable fluctuation range K prevents the vibration amplitude change caused by the cutter track change in the cutter damage online identification process from triggering a cutter damage state confirmation stage, and the data processing only needs to execute 1-time delta t time interval vibration signal calculation during vibration signal transition section judgment, so that the algorithm operation time is reduced.
And continuously intercepting the vibration signals at the time intervals of delta t, and respectively calculating the average value A of root mean square RMS (j) of each rotation.
Determining | A-M1If the value of | is larger than K, the transition section judgment is continuously executed within the range of K, and the cutter damage state confirmation stage is started when the value of | is larger than the range of K.
S4: confirming the damaged state of the cutter.
Selecting M sections of delta t time intervals of vibration signals, and calculating the average value M of root mean square of each rotation2。
Judgment | M2-M1Whether the value of | is greater than B × M1B is a set root mean square deviation factor, the magnitude of B is related to the allowable degree of tool breakage, if the requirements on the quality of the machined surface, the dimensional accuracy and the like are high, the value of the tool breakage depth or width is required to be small, the setting of the value of B is small, and the value of B is set to 0.5 in this example.
If the allowable range is not exceeded, the monitoring is continued, and if the allowable range is exceeded, a new cutter is replaced for machining.
S5: and replacing the new cutter with the same specification to continue to finish the program machining and simultaneously restarting the cutter damage monitoring process.

Claims (4)

1. A cutter damage identification method based on vibration monitoring is characterized in that: comprises the following steps:
s1: replacing a new tool in a structural member machining program to start vibration signal acquisition;
s2: calculating the mean value of the root mean square of each rotation of the vibration signal calibration section;
s201: setting a signal processing time interval Δ t as u × t0U is multiplied by 60/n, wherein u is the revolution number, and n is the rotation speed;
s202: calculating the root mean square value RMS (j) of each turn in the signal processing time interval delta t;
s203: selecting M sections of delta t vibration signals, calculating the average value of root mean square of each rotation in the range, and using the average value as a calibration value M1；
S3: performing vibration signal transition section discrimination
S301: setting an allowable fluctuation range K;
s302: continuously intercepting vibration signals at delta t time intervals, and respectively calculating an average value A of root mean square RMS (j) of each rotation;
s303: determine | A-M for each Δ t time interval1If the value of | is larger than K, continuing to execute the transition section judgment within the range of K, and entering a tool damage state confirmation stage when the value of | is larger than the range of K;
s4: stage of confirming damaged state of tool
S401: selecting M sections of delta t time intervals of vibration signals, and calculating the average value M of root mean square of each rotation2；
S402: judgment | M2-M1Whether the value of | is greater than B × M1B is a set root mean square deviation coefficient, and if the root mean square deviation coefficient does not exceed the set root mean square deviation coefficient, the monitoring is continued;
s5: if | M2-M1|＞B×M1And replacing the new cutter with the same specification to continue to finish the program machining, and restarting the cutter damage monitoring process when the new cutter is replaced.
2. The tool breakage identification method based on vibration monitoring as claimed in claim 1, wherein: in S3, if | A-M1And if not more than K, returning to S302 to process the vibration signal at the next time interval of delta t, and calculating the average value A of root mean square RMS (j) of each rotation.
3. The tool breakage identification method based on vibration monitoring as claimed in claim 1, wherein: in S4, if | M2-M1|≤B×M1And returning the flow to the step S302 of judging the transition section of the vibration signal, continuously intercepting the vibration signal at the time interval of delta t for processing, and calculating the average value A of root mean square RMS (j) of each rotation of the vibration signal.
4. The tool breakage identification method based on vibration monitoring as claimed in claim 1, wherein: the method for calculating the root mean square of the vibration amplitude of the vibration signal in each cutting time in S2 is as follows:
the number of samples per revolution, N, is calculated as:
Figure FDA0003128786160000021
wherein f issIs the sampling frequency of the vibration signal;
the root mean square rms (j) is calculated as:
Figure FDA0003128786160000022
wherein, x is the vibration amplitude in a certain direction, and j is the counting amount of the cutter in the cutting revolution;
selecting m sections of delta t vibration signals, and calibrating the average value of root-mean-square of each rotation:
Figure FDA0003128786160000023
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